Principal Investigator: Andee Rubin
CoPrincipal Investigator(s):
Organization: TERC Inc
Abstract:
The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advancing what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that showed the possibilities of the proposed new type of learning technology, and project teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and answer questions about how people learn with technology. An important issue in education is helping youth make sense of the scientific, technological, socio-scientific, and health data that is available. Technology exists for creating infographics to help others understand what data has to say, and this team’s Cyberlearning Exploration (EXP) Project showed that engaging high schoolers in infographic-based data journalism has potential to pique their interest in science and data analysis and to improve their scientific and mathematical literacy. In this follow-on project, the project team, which includes experts in educational technology, mathematics education, and learning, is extending, refining, and evaluating different ways of using technologies for infographic design to foster STEM engagement and learning. The end result will be a set of social practices and technology tools to support learning through data journalism, increased understanding of how to foster STEM literacy, a set of professional development materials and infrastructure for helping teachers and after-school facilitators learn to implement data journalism, and a set of guidelines for data providers about how to make their scientific data usable by teachers and youth and how to structure it to support youth’s developing data and science literacy skills. This work contributes to the emerging field of data science education — fostering learners’ ability to analyze and make sense of scientific, technological, and socio-scientific data and use it to solve problems of scientific, individual, and community importance.
In their Cyberlearning EXP project, this project team showed the viability and promise of using infographic-based data journalism as a way to support engagement of young adults (high-school students) in STEM and improve their scientific and mathematical literacy. In this follow-on project, they are refining their approach and implementing the proposed socio-technical system in a variety of different educational venues, some formal and some informal, and including, over the set of venues, learners with a wide variety of different capabilities and interests and from a wide variety of backgrounds and cultures, teachers and facilitators with a wide variety of skills, allowing exploration of issues across a variety of populations. The innovation is the socio-technical system and tools for learning STEM content through infographic design. Tools and practices are being designed to provide a meaningful and supportive context in which youth can contextualize STEM in their own lives and the lives of others, navigate the deluge of scientific data that is available, and learn through authentic communication of their understandings. Research and development are highly connected, and research focuses on how to arrange social processes and tools to enable youth to move from personal interests to topics they can research and make sense of, the barriers that m ay be encountered in moving from personal interests to scientific pursuits and how to overcome those barriers, and how to support youth so that they come to recognize connections between their own lives and the world around them and the world of science. The effort also contributes to the emerging field of data science education and will extract insights about how to foster such learning, provide guidelines to data providers about how to design data portals for the public, and suggest design guidelines for data science tools.
